Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3678
Missing cells6712
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory578.4 B

Variable types

Categorical10
Text3
Numeric10

Alerts

store room is highly imbalanced (55.7%) Imbalance
facing has 1045 (28.4%) missing values Missing
super_built_up_area has 1803 (49.0%) missing values Missing
built_up_area has 1988 (54.1%) missing values Missing
carpet_area has 1805 (49.1%) missing values Missing
area is highly skewed (γ1 = 29.73497381) Skewed
built_up_area is highly skewed (γ1 = 40.70657243) Skewed
carpet_area is highly skewed (γ1 = 24.33972046) Skewed
floorNum has 129 (3.5%) zeros Zeros
luxury_score has 462 (12.6%) zeros Zeros

Reproduction

Analysis started2025-07-12 14:14:35.459356
Analysis finished2025-07-12 14:14:58.758568
Duration23.3 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size219.9 KiB
flat
2819 
house
859 

Length

Max length5
Median length4
Mean length4.2335508
Min length4

Characters and Unicode

Total characters15571
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowhouse

Common Values

ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Length

2025-07-12T19:44:58.900192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:44:59.202923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15571
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15571
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%
Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size265.3 KiB
2025-07-12T19:44:59.580624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.870819
Min length1

Characters and Unicode

Total characters62034
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowsupertech araville
2nd rowindiabulls centrum park
3rd rowshapoorji pallonji joyville gurugram
4th rowsignature global city 81
5th rowindependent
ValueCountFrequency (%)
independent 491
 
5.1%
the 351
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 151
 
1.6%
heights 134
 
1.4%
Other values (783) 7498
77.5%
2025-07-12T19:45:00.295535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6713
 
10.8%
6005
 
9.7%
a 5863
 
9.5%
r 4173
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3721
 
6.0%
s 3474
 
5.6%
l 2944
 
4.7%
o 2756
 
4.4%
Other values (31) 18389
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55484
89.4%
Space Separator 6005
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6713
12.1%
a 5863
 
10.6%
r 4173
 
7.5%
n 4164
 
7.5%
i 3832
 
6.9%
t 3721
 
6.7%
s 3474
 
6.3%
l 2944
 
5.3%
o 2756
 
5.0%
d 2488
 
4.5%
Other values (16) 15356
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
9 13
 
2.5%
0 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6005
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55484
89.4%
Common 6550
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6713
12.1%
a 5863
 
10.6%
r 4173
 
7.5%
n 4164
 
7.5%
i 3832
 
6.9%
t 3721
 
6.7%
s 3474
 
6.3%
l 2944
 
5.3%
o 2756
 
5.0%
d 2488
 
4.5%
Other values (16) 15356
27.7%
Common
ValueCountFrequency (%)
6005
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
9 13
 
0.2%
0 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6713
 
10.8%
6005
 
9.7%
a 5863
 
9.5%
r 4173
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3721
 
6.0%
s 3474
 
5.6%
l 2944
 
4.7%
o 2756
 
4.4%
Other values (31) 18389
29.6%

sector
Text

Distinct115
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
2025-07-12T19:45:00.645134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3178358
Min length3

Characters and Unicode

Total characters34271
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 79
2nd rowsector 103
3rd rowsector 102
4th rowsector 81
5th rowsector 1
ValueCountFrequency (%)
sector 3449
46.7%
road 178
 
2.4%
sohna 166
 
2.2%
102 107
 
1.4%
85 107
 
1.4%
92 100
 
1.4%
69 92
 
1.2%
90 89
 
1.2%
65 87
 
1.2%
81 87
 
1.2%
Other values (107) 2923
39.6%
2025-07-12T19:45:01.192673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1075
 
3.1%
0 801
 
2.3%
8 779
 
2.3%
Other values (21) 6209
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23307
68.0%
Decimal Number 7257
 
21.2%
Space Separator 3707
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3804
16.3%
s 3694
15.8%
r 3694
15.8%
e 3548
15.2%
c 3500
15.0%
t 3460
14.8%
a 698
 
3.0%
d 249
 
1.1%
n 230
 
1.0%
h 203
 
0.9%
Other values (10) 227
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1075
14.8%
0 801
11.0%
8 779
10.7%
9 762
10.5%
6 740
10.2%
7 683
9.4%
2 679
9.4%
3 666
9.2%
5 590
8.1%
4 482
6.6%
Space Separator
ValueCountFrequency (%)
3707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23307
68.0%
Common 10964
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3804
16.3%
s 3694
15.8%
r 3694
15.8%
e 3548
15.2%
c 3500
15.0%
t 3460
14.8%
a 698
 
3.0%
d 249
 
1.1%
n 230
 
1.0%
h 203
 
0.9%
Other values (10) 227
 
1.0%
Common
ValueCountFrequency (%)
3707
33.8%
1 1075
 
9.8%
0 801
 
7.3%
8 779
 
7.1%
9 762
 
7.0%
6 740
 
6.7%
7 683
 
6.2%
2 679
 
6.2%
3 666
 
6.1%
5 590
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1075
 
3.1%
0 801
 
2.3%
8 779
 
2.3%
Other values (21) 6209
18.1%

price
Real number (ℝ)

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5330948
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:01.461104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9804152
Coefficient of variation (CV)1.1765905
Kurtosis14.93605
Mean2.5330948
Median Absolute Deviation (MAD)0.72
Skewness3.2794411
Sum9273.66
Variance8.882875
MonotonicityNot monotonic
2025-07-12T19:45:01.907887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.5%
2 52
 
1.4%
0.95 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3059
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13891.034
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:02.164848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4716
Q16818
median9020
Q313878
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7060

Descriptive statistics

Standard deviation23207.107
Coefficient of variation (CV)1.6706537
Kurtosis186.97616
Mean13891.034
Median Absolute Deviation (MAD)2793
Skewness11.438647
Sum50855074
Variance5.3856981 × 108
MonotonicityNot monotonic
2025-07-12T19:45:02.466935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
6666 13
 
0.4%
22222 13
 
0.4%
11111 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3510
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

Skewed 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2887.6976
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:02.745799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11230
median1733
Q32300
95-th percentile4246
Maximum875000
Range874950
Interquartile range (IQR)1070

Descriptive statistics

Standard deviation23164.373
Coefficient of variation (CV)8.0217445
Kurtosis942.28496
Mean2887.6976
Median Absolute Deviation (MAD)533
Skewness29.734974
Sum10571861
Variance5.3658816 × 108
MonotonicityNot monotonic
2025-07-12T19:45:03.012391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
1950 43
 
1.2%
3240 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3268
88.9%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size399.5 KiB
2025-07-12T19:45:03.493724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.229473
Min length12

Characters and Unicode

Total characters199456
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1848 ?
Unique (%)50.2%

Sample

1st rowCarpet area: 1945 (180.7 sq.m.)
2nd rowSuper Built up area 2875(267.1 sq.m.)Carpet area: 2570 sq.ft. (238.76 sq.m.)
3rd rowSuper Built up area 1852(172.06 sq.m.)Carpet area: 1128 sq.ft. (104.79 sq.m.)
4th rowCarpet area: 546 (50.73 sq.m.)
5th rowBuilt Up area: 4500 (418.06 sq.m.)
ValueCountFrequency (%)
area 5574
18.5%
sq.m 3656
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 684
 
2.3%
plot 681
 
2.3%
Other values (2846) 8702
28.9%
2025-07-12T19:45:04.265946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26468
 
13.3%
. 20392
 
10.2%
a 13157
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9205
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6770
 
3.4%
Other values (25) 82359
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82770
41.5%
Decimal Number 47142
23.6%
Space Separator 26468
 
13.3%
Other Punctuation 23410
 
11.7%
Uppercase Letter 8594
 
4.3%
Close Punctuation 5536
 
2.8%
Open Punctuation 5536
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13157
15.9%
r 9458
11.4%
e 9322
11.3%
s 7568
9.1%
q 7432
9.0%
t 7325
8.8%
u 6770
8.2%
p 6768
8.2%
m 5545
6.7%
l 3701
 
4.5%
Other values (5) 5724
6.9%
Decimal Number
ValueCountFrequency (%)
1 9205
19.5%
0 6628
14.1%
2 5689
12.1%
5 4716
10.0%
3 3960
8.4%
4 3711
7.9%
6 3676
 
7.8%
7 3254
 
6.9%
8 3158
 
6.7%
9 3145
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3020
35.1%
S 1875
21.8%
C 1873
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20392
87.1%
: 3018
 
12.9%
Space Separator
ValueCountFrequency (%)
26468
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5536
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108092
54.2%
Latin 91364
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13157
14.4%
r 9458
10.4%
e 9322
10.2%
s 7568
8.3%
q 7432
8.1%
t 7325
8.0%
u 6770
7.4%
p 6768
7.4%
m 5545
 
6.1%
l 3701
 
4.1%
Other values (10) 14318
15.7%
Common
ValueCountFrequency (%)
26468
24.5%
. 20392
18.9%
1 9205
 
8.5%
0 6628
 
6.1%
2 5689
 
5.3%
) 5536
 
5.1%
( 5536
 
5.1%
5 4716
 
4.4%
3 3960
 
3.7%
4 3711
 
3.4%
Other values (5) 16251
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26468
 
13.3%
. 20392
 
10.2%
a 13157
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9205
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6770
 
3.4%
Other values (25) 82359
41.3%

bedRoom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3597064
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:04.500572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8975034
Coefficient of variation (CV)0.5647825
Kurtosis18.215499
Mean3.3597064
Median Absolute Deviation (MAD)1
Skewness3.4853698
Sum12357
Variance3.600519
MonotonicityNot monotonic
2025-07-12T19:45:04.712290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 943
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
12 28
 
0.8%
7 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 943
25.6%
3 1496
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4241436
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:04.916744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9479448
Coefficient of variation (CV)0.56888526
Kurtosis17.544566
Mean3.4241436
Median Absolute Deviation (MAD)1
Skewness3.2490529
Sum12594
Variance3.794489
MonotonicityNot monotonic
2025-07-12T19:45:05.137633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1048
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1048
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size209.5 KiB
3+
1172 
3
1074 
2
885 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3186514
Min length1

Characters and Unicode

Total characters4850
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3+
3rd row3
4th row2
5th row0

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1074
29.2%
2 885
24.1%
1 365
 
9.9%
0 182
 
4.9%

Length

2025-07-12T19:45:05.423728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:45:05.662120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2246
61.1%
2 885
 
24.1%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 885
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2246
61.1%
2 885
 
24.1%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 885
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 885
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7969391
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:05.965151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0121557
Coefficient of variation (CV)0.8845387
Kurtosis4.5164183
Mean6.7969391
Median Absolute Deviation (MAD)3
Skewness1.6940035
Sum24870
Variance36.146016
MonotonicityNot monotonic
2025-07-12T19:45:06.282371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 494
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 494
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

Missing 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size229.5 KiB
East
624 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.837068
Min length4

Characters and Unicode

Total characters18002
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowWest
3rd rowNorth
4th rowNorth-East
5th rowNorth-East

Common Values

ValueCountFrequency (%)
East 624
17.0%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2025-07-12T19:45:06.559374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:45:06.777734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
east 624
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3775
21.0%
s 2015
11.2%
o 1760
9.8%
h 1760
9.8%
E 1420
 
7.9%
a 1420
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13085
72.7%
Uppercase Letter 3775
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3775
28.8%
s 2015
15.4%
o 1760
13.5%
h 1760
13.5%
a 1420
 
10.9%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1420
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16860
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3775
22.4%
s 2015
12.0%
o 1760
10.4%
h 1760
10.4%
E 1420
 
8.4%
a 1420
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3775
21.0%
s 2015
11.2%
o 1760
9.8%
h 1760
9.8%
E 1420
 
7.9%
a 1420
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size252.8 KiB
Relatively New
1646 
New Property
594 
Moderately Old
563 
Undefined
306 
Old Property
303 

Length

Max length18
Median length14
Mean length13.385536
Min length9

Characters and Unicode

Total characters49232
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowRelatively New
3rd rowRelatively New
4th rowUnder Construction
5th rowUndefined

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 594
 
16.2%
Moderately Old 563
 
15.3%
Undefined 306
 
8.3%
Old Property 303
 
8.2%
Under Construction 266
 
7.2%

Length

2025-07-12T19:45:07.090332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:45:07.303237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2240
31.8%
relatively 1646
23.3%
property 897
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 306
 
4.3%
under 266
 
3.8%
construction 266
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2307
 
4.7%
N 2240
 
4.5%
w 2240
 
4.5%
i 2218
 
4.5%
Other values (15) 14068
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38810
78.8%
Uppercase Letter 7050
 
14.3%
Space Separator 3372
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8433
21.7%
l 4721
12.2%
t 3638
9.4%
y 3106
 
8.0%
r 2889
 
7.4%
d 2307
 
5.9%
w 2240
 
5.8%
i 2218
 
5.7%
a 2209
 
5.7%
o 1992
 
5.1%
Other values (7) 5057
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2240
31.8%
R 1646
23.3%
P 897
12.7%
O 866
 
12.3%
U 572
 
8.1%
M 563
 
8.0%
C 266
 
3.8%
Space Separator
ValueCountFrequency (%)
3372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45860
93.2%
Common 3372
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8433
18.4%
l 4721
 
10.3%
t 3638
 
7.9%
y 3106
 
6.8%
r 2889
 
6.3%
d 2307
 
5.0%
N 2240
 
4.9%
w 2240
 
4.9%
i 2218
 
4.8%
a 2209
 
4.8%
Other values (14) 11859
25.9%
Common
ValueCountFrequency (%)
3372
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2307
 
4.7%
N 2240
 
4.5%
w 2240
 
4.5%
i 2218
 
4.5%
Other values (15) 14068
28.6%

super_built_up_area
Real number (ℝ)

Missing 

Distinct593
Distinct (%)31.6%
Missing1803
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:07.571510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2025-07-12T19:45:07.832965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
1578 25
 
0.7%
2000 25
 
0.7%
2150 22
 
0.6%
1640 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1803
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

Missing  Skewed 

Distinct644
Distinct (%)38.1%
Missing1988
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean2379.5858
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:08.082564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.88
Coefficient of variation (CV)7.5403375
Kurtosis1667.8704
Mean2379.5858
Median Absolute Deviation (MAD)650
Skewness40.706572
Sum4021500
Variance3.2194695 × 108
MonotonicityNot monotonic
2025-07-12T19:45:08.527325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1387
37.7%
(Missing) 1988
54.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

Missing  Skewed 

Distinct733
Distinct (%)39.1%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2528.133
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:08.904316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1837
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)953

Descriptive statistics

Standard deviation22793.791
Coefficient of variation (CV)9.016057
Kurtosis604.86099
Mean2528.133
Median Absolute Deviation (MAD)475
Skewness24.33972
Sum4735193
Variance5.1955691 × 108
MonotonicityNot monotonic
2025-07-12T19:45:09.195818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (723) 1579
42.9%
(Missing) 1805
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2973 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Length

2025-07-12T19:45:09.454581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:45:09.643963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2350 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Length

2025-07-12T19:45:09.860109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:45:10.069528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3340 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Length

2025-07-12T19:45:10.281247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:45:10.826790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3022 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Length

2025-07-12T19:45:11.578778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:45:11.901913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3273 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Length

2025-07-12T19:45:12.216075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:45:12.459425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2420 
1
1050 
2
 
208

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2420
65.8%
1 1050
28.5%
2 208
 
5.7%

Length

2025-07-12T19:45:12.684489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-12T19:45:12.960713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2420
65.8%
1 1050
28.5%
2 208
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 2420
65.8%
1 1050
28.5%
2 208
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2420
65.8%
1 1050
28.5%
2 208
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2420
65.8%
1 1050
28.5%
2 208
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2420
65.8%
1 1050
28.5%
2 208
 
5.7%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.501903
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-12T19:45:13.589032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.056072
Coefficient of variation (CV)0.74202322
Kurtosis-0.87982581
Mean71.501903
Median Absolute Deviation (MAD)38
Skewness0.45950152
Sum262984
Variance2814.9468
MonotonicityNot monotonic
2025-07-12T19:45:14.051793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
165 55
 
1.5%
38 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2314
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-07-12T19:44:55.415435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:37.464228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:39.638286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:41.844719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:43.720017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:45.674773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:47.599484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:49.537172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:51.691488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:53.550965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:55.600341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:37.680118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:39.829014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:42.027664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:43.914514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:45.862272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:47.775307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:49.704928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:51.876634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:53.723152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:55.775692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:37.946984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:40.136587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:42.203192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:44.115324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:46.054877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:47.959540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:49.910380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:52.080466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:53.902672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:55.940252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:38.143456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:40.358992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:42.376449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:44.290855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:46.234197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:48.134977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:50.404587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:52.262452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:54.133092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:56.170635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:38.534906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:40.575310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:42.570845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:44.493625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:46.437654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:48.349403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:50.600095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:52.470899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:54.327535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:56.368014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:38.721157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:40.772749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:42.769827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:44.695086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:46.644845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:48.541767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:50.775926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:52.677881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:54.524198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:56.535665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:38.899295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:40.970252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:42.934024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:44.882781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:46.825778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:48.707325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:50.948993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:52.850596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:54.702534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:56.712745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:39.077815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:41.176666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:43.123371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:45.069764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:47.000349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:48.877481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:51.130545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:53.000055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:54.882432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:56.906570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:39.267346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:41.393085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:43.311058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:45.275365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:47.210748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:49.106695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:51.288771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:53.196901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:55.043620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:57.083094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:39.452112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:41.644255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:43.529891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:45.471315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:47.403020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:49.317512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:51.473882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:53.349495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-07-12T19:44:55.218400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2025-07-12T19:44:57.391301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-12T19:44:58.049223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-12T19:44:58.551091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatsupertech aravillesector 791.356940.01945.0Carpet area: 1945 (180.7 sq.m.)3334.0NaNRelatively NewNaNNaN1945.00000000
1flatindiabulls centrum parksector 1032.369182.02570.0Super Built up area 2875(267.1 sq.m.)Carpet area: 2570 sq.ft. (238.76 sq.m.)463+8.0EastRelatively New2875.0NaN2570.0010001159
2flatshapoorji pallonji joyville gurugramsector 1021.759449.01852.0Super Built up area 1852(172.06 sq.m.)Carpet area: 1128 sq.ft. (104.79 sq.m.)33310.0WestRelatively New1852.0NaN1128.011011049
3flatsignature global city 81sector 810.8014652.0546.0Carpet area: 546 (50.73 sq.m.)2224.0NorthUnder ConstructionNaNNaN546.010000036
4houseindependentsector 14.7510556.04500.0Built Up area: 4500 (418.06 sq.m.)6704.0NaNUndefinedNaN4500.0NaN0000000
5flatsilverglades the meliasohna road1.017000.01443.0Super Built up area 1450(134.71 sq.m.)Carpet area: 950 sq.ft. (88.26 sq.m.)22312.0NaNUnder Construction1450.0NaN950.010000080
6flatdlf the ultimasector 812.4011412.02103.0Super Built up area 2103(195.38 sq.m.)Built Up area: 1650 sq.ft. (153.29 sq.m.)Carpet area: 1257 sq.ft. (116.78 sq.m.)333+12.0North-EastRelatively New2103.01650.01257.001000249
7flatgodrej nature plussector 331.409333.01500.0Super Built up area 1500(139.35 sq.m.)Carpet area: 1385 sq.ft. (128.67 sq.m.)22314.0NaNUnder Construction1500.0NaN1385.000000067
8flatansals duplex flatsector 20.957307.01300.0Carpet area: 1300 (120.77 sq.m.)2321.0North-EastOld PropertyNaNNaN1300.010000120
9flattulip violetsector 691.758882.01970.0Super Built up area 1970(183.02 sq.m.)44112.0North-WestRelatively New1970.0NaNNaN00000086
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3789flatsmart world gemssector 890.908145.01105.0Built Up area: 1105 (102.66 sq.m.)2221.0NaNUndefinedNaN1105.0NaN00000045
3790flatrof anandasector 950.3858.065517.0Carpet area: 64529 (5994.94 sq.m.)22210.0EastNew PropertyNaNNaN64529.010000015
3791flatcentral park flower valley aqua front towerssector 331.709449.01799.0Super Built up area 1799(167.13 sq.m.)Carpet area: 1590 sq.ft. (147.72 sq.m.)333+5.0EastNew Property1799.0NaN1590.000000144
3792flatsuncity avenuesector 1020.489022.0532.0Super Built up area 632(58.71 sq.m.)Carpet area: 532 sq.ft. (49.42 sq.m.)2215.0North-EastRelatively New632.0NaN532.0001000159
3793housedlf gardencity enclavesector 931.3011284.01152.0Plot area 1152(107.02 sq.m.)3213.0NaNRelatively NewNaN1152.0NaN00000050
3794flatumang monsoon breezesector 780.804836.01654.0Super Built up area 1654(153.66 sq.m.)Built Up area: 1472 sq.ft. (136.75 sq.m.)Carpet area: 1310 sq.ft. (121.7 sq.m.)3227.0SouthModerately Old1654.01472.01310.0000100102
3797flatats marigoldsector 891.709714.01750.0Carpet area: 1750 (162.58 sq.m.)333+8.0NorthUnder ConstructionNaNNaN1750.010000065
3798househero homessector 1041.9521195.0920.0Plot area 920(85.47 sq.m.)353+2.0NaNModerately OldNaN920.0NaN00000064
3801flatemaar mgf emerald estatesector 651.7021013.0809.0Super Built up area 1395(129.6 sq.m.)Carpet area: 809 sq.ft. (75.16 sq.m.)3337.0South-EastRelatively New1395.0NaN809.0100001116
3802flattulip violetsector 691.959701.02010.0Super Built up area 2010(186.74 sq.m.)4420.0WestRelatively New2010.0NaNNaN000000157